Many fashion products are sold online by a large number of retailers, but finding a desired product is a “needle in a haystack” problem.
Consumers search for fashion products based on their visual style attributes (e.g. sleeve length, pattern, silhouette, and the like); however general search engines (e.g. Google, Bing, Yahoo, and the like), as well as retailer-specific search engines (e.g. Nordstrom, Macy's, Neiman Marcus, and the like) are only able to index and retrieve products based on their textual metadata. Style attributes that are apparent to the human eye but are not captured in the textual metadata will not be indexed, and therefore not returned in searches for those attributes.
The algorithm most frequently used by search engines is tf-idf (text frequency-inverse document frequency). Tf-idf favors concise text that only contains information that is relevant to the search query. In contrast, language used to describe fashion products often contains extraneous editorial content (e.g. “this dress is perfect for all those fun warm-weather occasions—think showers, brunches and summer parties”) that inflates the document's word count and thereby reduces its relevance score.
The language to describe a product's style attributes varies much more widely for fashion products than it does for other types of products such as electronics, appliances, tools, and the like. Search engines are unable to recognize fashion-specific synonyms and therefore do not return relevant products unless they match the query keywords (e.g. a search for “batwing sleeve sweater” will not return documents containing “dolman sleeve sweater” even though those terms are synonymous.)
Creating textual product metadata is currently a task that requires human effort. Depending on the resources dedicated to creating the metadata, its quality and comprehensiveness varies widely by manufacturer and/or vendor. Since search engines only have a product's textual metadata to work with, products with low-quality or incomplete textual metadata are ranked lower than those with more comprehensive metadata, and therefore rarely reach their target customers.
Textual product metadata, as well as the search engines that index it, are language-specific. A French-language search for “robe patineuse” will not return English-language documents containing the expression “skater dress” even though those phrases have the same meaning. To sell their merchandise to speakers of multiple languages, multi-national retailers have to translate their textual product metadata and have each language translation indexed separately. This process is expensive because it requires idiomatic knowledge of fashion terminology for each target language.